LITTORAL POWER SYSTEMS, INC. (LPS) WILL DEVELOP AND DEMONSTRATE MACHINE-LEARNING-ENABLED SUPERVISORY CONTROLS ON A WAVE ENERGY CONVERTOR (WEC) DEVICE. THE OVERALL GOAL OF THE PROJECT IS TO DEMONSTRATE THAT, IN REAL-WORLD SEA STATES, THE USE OF MACHINE LEARNING (ML)-ENABLED SUPERVISORY CONTROLS CAN GREATLY INCREASE THE COST EFFICIENCY OF A WAVE ENERGY CONVERTER (WEC) AS COMPARED TO CONVENTIONAL PASSIVE CONTROLS. THE LPS NEURALWEC, A WIDE BEAM POINT ABSORBER (THE PREDECESSOR OF WHICH WAS A FINALIST IN THE DOE WAVE ENERGY PRIZE), WILL BE DEPLOYED AND TESTED IN THE OPEN OCEAN AT THE PACWAVE TEST FACILITY WITH AND WITHOUT THE PROPOSED SUPERVISORY CONTROLS. DEPLOYMENT AT PACWAVE FACILITATES DEMONSTRATION OF THESE IMPROVEMENTS IN (I) COMPLEX SEA STATES WHERE THE WAVE SHAPES AND INSTANTANEOUS FORCES ON THE DEVICE ARE NOT KNOWN BEFOREHAND, AND (II) LONG-PERIOD OPEN OCEAN WAVES, WHERE MOST CONVENTIONAL POINT ABSORBER WECS HAVE GREAT DIFFICULTY PRODUCING ENERGY EFFICIENTLY. TO ACHIEVE THESE GOALS, LPS WILL ALSO MATURE THE NEURALWEC DESIGN ACCORDING TO INTERNATIONAL STANDARDS, DEVELOP DEPLOYMENT AND TEST PLANS, EVALUATE COMMERCIAL RISKS, AND PROVIDE DATA TO ADVANCE THE WAVE INDUSTRY TOWARD ACHIEVING COST COMPETITIVENESS IN BOTH NON-GRID AND UTILITY-SCALE MARKETS.